




from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE:     List[str]									= TypeVar('''T''')



class    lowercase_		(Generic[T] ):



						def __init__(			self	:   List[Any]					,							snake_case__	:   int = True  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}  # dictionary of lists
								SCREAMING_SNAKE_CASE_					      =							directed



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Union[str, Any]					,							snake_case__	:   Tuple  ):

								"""simple docstring"""

								if not self.directed:  # For undirected graphs
										# if both source vertex and destination vertex are both present in the
										# adjacency list, add destination vertex to source vertex list of adjacent
										# vertices and add source vertex to destination vertex list of adjacent
										# vertices.
										if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(UpperCamelCase__  )
												self.adj_list[destination_vertex].append(UpperCamelCase__  )
										# if only source vertex is present in adjacency list, add destination vertex
										# to source vertex list of adjacent vertices, then create a new vertex with
										# destination vertex as key and assign a list containing the source vertex
										# as it's first adjacent vertex.
										elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(UpperCamelCase__  )
												SCREAMING_SNAKE_CASE_					      =							[source_vertex]
										# if only destination vertex is present in adjacency list, add source vertex
										# to destination vertex list of adjacent vertices, then create a new vertex
										# with source vertex as key and assign a list containing the source vertex
										# as it's first adjacent vertex.
										elif destination_vertex in self.adj_list:
												self.adj_list[destination_vertex].append(UpperCamelCase__  )
												SCREAMING_SNAKE_CASE_					      =							[destination_vertex]
										# if both source vertex and destination vertex are not present in adjacency
										# list, create a new vertex with source vertex as key and assign a list
										# containing the destination vertex as it's first adjacent vertex also
										# create a new vertex with destination vertex as key and assign a list
										# containing the source vertex as it's first adjacent vertex.
										else:
												SCREAMING_SNAKE_CASE_					      =							[destination_vertex]
												SCREAMING_SNAKE_CASE_					      =							[source_vertex]
								else:  # For directed graphs
										# if both source vertex and destination vertex are present in adjacency
										# list, add destination vertex to source vertex list of adjacent vertices.
										if source_vertex in self.adj_list and destination_vertex in self.adj_list:
												self.adj_list[source_vertex].append(UpperCamelCase__  )
										# if only source vertex is present in adjacency list, add destination
										# vertex to source vertex list of adjacent vertices and create a new vertex
										# with destination vertex as key, which has no adjacent vertex
										elif source_vertex in self.adj_list:
												self.adj_list[source_vertex].append(UpperCamelCase__  )
												SCREAMING_SNAKE_CASE_					      =							[]
										# if only destination vertex is present in adjacency list, create a new
										# vertex with source vertex as key and assign a list containing destination
										# vertex as first adjacent vertex
										elif destination_vertex in self.adj_list:
												SCREAMING_SNAKE_CASE_					      =							[destination_vertex]
										# if both source vertex and destination vertex are not present in adjacency
										# list, create a new vertex with source vertex as key and a list containing
										# destination vertex as it's first adjacent vertex. Then create a new vertex
										# with destination vertex as key, which has no adjacent vertex
										else:
												SCREAMING_SNAKE_CASE_					      =							[destination_vertex]
												SCREAMING_SNAKE_CASE_					      =							[]

								return self






						def __repr__(			self	:   List[str]  ):

								"""simple docstring"""

								return pformat(self.adj_list  )







'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''image_processor''', '''tokenizer''']
  _a  : List[Any]      			=     '''ViTImageProcessor'''
  _a  : int      			=     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'   ,	UpperCamelCase__   ,	)
                a_						     =			kwargs.pop('feature_extractor'	)

         a_						     =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'	)
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'	)

         super().__init__(UpperCamelCase__   ,	UpperCamelCase__	)







  def __call__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         if text is None and visual_prompt is None and images is None:
                raise ValueError('You have to specify either text, visual prompt or images.'	)

         if text is not None and visual_prompt is not None:
                raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'	)

         if text is not None:
                a_						     =			self.tokenizer(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if images is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None and images is not None:
                a_						     =			{
                    'pixel_values': image_features.pixel_values,
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                a_						     =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                a_						     =			{
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**UpperCamelCase__	)   ,	tensor_type=UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.batch_decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor_class







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor




